Heterogeneity of hypoxemia severity according to oxygenation index in COVID-19 pneumonia (preprint)

ABSTRACT Objective To compare hypoxemia severity of patients with COVID-19 pneumonia that arrive at an emergency department as classified by three oxygenation indexes. Design Retrospective analysis of pulse oximeter saturation and arterial blood gas analysis obtained at arrival. Setting Tertiary referral hospital in Mexico City converted early in the pandemic to a COVID-19 center. Patients and measurements A total of 2,960 patients with suspected COVID-19 pneumonia were admitted to the emergency department from April 2020 until March 2021. Pulse oximeter saturation and arterial blood gas analysis was obtained in all of them. Pulse oximeter saturation (SpO2) to inspired oxygen fraction ratio (FiO2), oxygen saturation in arterial blood (SatO2) to FiO2 ratio, and oxygen pressure in arterial blood to FiO2 ratio were calculated for every patient. Interventions None. Main Results A strong correlation was seen between PaO2/FiO2 & SpO2/FiO2 (rho = 0.6, p < 0.001), and SatO2/FiO2 & SpO2/FiO2 (rho = 0.65, p < 0.001), while a very strong correlation was seen between PaO2/FiO2 & SatO2/FiO2 (rho = 0.88, p < 0.001). When classifying severity by quantiles, considerable cross-over was observed when comparing oxygenation indexes, as only 785 (26.5%) patients were in the same quintile across the three indexes. Conclusions Hypoxemia severity is heterogeneous according to the oxygenation index utilized. This limits their usefulness as sole markers of severity, as inter-observer variability, especially on FiO2 estimation, and different practices limit consistent follow up and treatment decisions.

Hospital saturation and risk of death without receiving mechanical ventilation in hospitalized COVID-19 patients: a city-wide analysis (preprint)

Background Pneumonia is the hallmark of severe COVID-19, with supplemental oxygen requirement being the main indication for hospitalization. Refractory hypoxemia in these patients requires invasive mechanical ventilation (IMV) otherwise, death is imminent. In places with a high disease burden, availability of critical care experts, beds, or resources is challenged and many patients could die without receiving them. Methods We performed a retrospective cohort study using open databases from Mexico City about suspected or confirmed COVID-19 patients, health system saturation, and deaths between May 8 th , 2020, and January 5 th , 2021. After building a directed acyclic graph, we performed a binary logistic regression to identify the association between proposed causal variables and dying without receiving IMV (the outcome). Results We included 33 805 hospitalized patients with suspected or confirmed COVID-19, of which 19 820 (58.6%) did not require IMV and survived, 5416 (16.1%) required and received IMV, and 8569 (25.3%) required IMV but died without receiving it. Saturation of IMV-capable beds did not increase the odds of the outcome (odds ratio 1.07, 95% confidence interval 0.94-1.22 of 90%vs50% occupancy), while general bed saturation (2, 1.86-2.14 of 90%vs50% occupancy) and IMV-capable to general bed ratio (1.64, 1.52-1.77 for a ratio of 2vs0.5) did. Private healthcare decreased the odds of the outcome (0.12, 0.08-0.17) and dyspnea increased them (1.33, 1.19-1.9). Conclusions In Mexico City, increased general hospital bed saturation and IMV-capable to general bed ratio were associated with a higher risk of dying without receiving IMV. Private healthcare was the most protective factor. Key messages Hospital saturation has been a central feature of public health messaging, but it is not known how outcomes relate to hospital saturation or capacity. In Mexico City, 90% of COVID-19 patients requiring mechanical ventilation died but less than half received it. Higher general bed saturation and an increased ratio of IMV-capable beds to general beds increased the probability of dying without being intubated while receiving private healthcare decreased this probability. Having available beds to intubate patients is possible thanks to the conversion of general beds, however, still yields suboptimal critical care.

Froin's syndrome secondary to disseminated staphylococcus infection in a COVID-19 patient. (preprint)

Bacterial CNS are rare in COVID 19 patients. We describe a patient with severe pneumonia and meningitis that developed Froin’s syndrome by Staphylococcus aureus; this suggests that immune dysregulation in patients with COVID-19 facilitate disseminating commensal bacterial infections from the skin or the mucous membranes. Keywords: Meningitis, Froin’s syndrome, COVID-19.

Prospective predictive performance comparison between Clinical Gestalt and validated COVID-19 mortality scores (preprint)

ABSTRACT Background Most COVID-19 mortality scores were developed in the early months of the pandemic and now available evidence-based interventions have helped reduce its lethality. It has not been evaluated if the original predictive performance of these scores holds true nor compared it against Clinical Gestalt predictions. We tested the current predictive accuracy of six COVID-19 scores and compared it with Clinical Gestalt predictions. Methods 200 COVID-19 patients were enrolled in a tertiary hospital in Mexico City between September and December 2020. Clinical Gestalt predictions of death (as a percentage) and LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV and NEWS2 were obtained at admission. We calculated the AUC of each score and compared it against Clinical Gestalt predictions and against their respective originally reported value. Results 106 men and 60 women aged 56+/-9 and with confirmed COVID-19 were included in the analysis. The observed AUC of all scores was significantly lower than originally reported; LOW-HARM 0.96 (0.94-0.98) vs 0.76 (0.69-0.84), qSOFA 0.74 (0.65-0.81) vs 0.61 (0.53-0.69), MSL-COVID-19 0.72 (0.69-0.75) vs 0.64 (0.55-0.73) NUTRI-CoV 0.79 (0.76-0.82) vs 0.60 (0.51-0.69), NEWS2 0.84 (0.79-0.90) vs 0.65 (0.56-0.75), Neutrophil-Lymphocyte ratio 0.74 (0.62-0.85) vs 0.65 (0.57-0.73). Clinical Gestalt predictions were non-inferior to mortality scores (AUC=0.68 (0.59-0.77)). Adjusting the LOW-HARM score with locally derived likelihood ratios did not improve its performance. However, some scores performed better than Clinical Gestalt predictions when clinician’s confidence of prediction was <80%. Conclusion No score was significantly better than Clinical Gestalt predictions. Despite its subjective nature, Clinical Gestalt has relevant advantages for predicting COVID-19 clinical outcomes.

THE LOW-HARM SCORE FOR PREDICTING MORTALITY IN PATIENTS DIAGNOSED WITH COVID-19: A MULTICENTRIC VALIDATION STUDY (preprint)

- ImportanceMany COVID-19 prognostic factors for disease severity have been identified and many scores have already been proposed to predict death and other outcomes. However, hospitals in developing countries often cannot measure some of the variables that have been reported as useful. - ObjectiveTo assess the sensitivity, specificity, and predictive values of the novel LOW-HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury). - DesignThe score was designed using data from already published cohorts of patients diagnosed with COVID-19. Afterwards, it was calculated it in 438 consecutive hospital admissions at twelve different institutions in ten different cities in Mexico. - SettingTwelve hospitals in ten different cities in Mexico. - ParticipantsData from 438 patients was collected. Data from 400 patients (200 deaths and 200 survivors) was included in the analysis. - ExposureAll patients had an infection with SARS-CoV-2 confirmed by PCR. - Main OutcomeThe sensitivity, specificity, and predictive values of different cut-offs of the LOW-HARM score to predict death. - ResultsMean scores at admission and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 10 (SD: 17) vs 70 (SD: 28). The overall AUC of the model was 95%. A cut-off > 65 points had a specificity of 98% and a positive predictive value of 96%. More than a third of the cases (36%) in the sample had a LOW-HARM score > 65 points. - Conclusions and relevanceThe LOW-HARM score measured at admission is highly specific and useful for predicting mortality. It is easy to calculate and can be updated with individual clinical progression. KEY POINTSO_ST_ABSQuestionC_ST_ABSIs it possible to predict mortality in patients diagnosed with COVID-19 using easy-to-access and easy-to-measure variables? FindingsThe LOW-HARM score (Lymphopenia, Oxygen saturation, White blood cells, Hypertension, Age, Renal injury, and Myocardial injury) is a one-hundred-point score that, when measured at admission, had an overall AUC of 95% for predicting mortality. A cut-off of [≥] 65 points had a specificity of 98% and a positive predictive value of 96%. MeaningThe LOW-HARM score measured at admission is highly specific and useful for predicting mortality in patients diagnosed with COVID-19. In our sample, more than a third of patients met the proposed cut-off.